{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Backprojection (2D)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The backprojection imaging process is as follows;\n",
    "\n",
    "1. Construct the spatial domain target density array $f (x_i , y_j)$.\n",
    "2. Pulse compress return signals and perform filtering.\n",
    "3. Upsample the returns for better image quality. Upsample factors can be large $( > 100 )$.\n",
    "4. For a given pixel point, $(x_i , y_j)$, find the nearest upsampled time point (interpolate) and add the value of the filtered data to the array component $f (x_i , y_j)$.\n",
    "5. Repeat for all pixel locations $(x_i , y_j)$ and all pulses.\n",
    "\n",
    "The filtered backprojection algorithm has several advantages.  It is computationally efficient and employs the fast Fourier transform (FFT).  It is highly parallelizable as each projection's contribution and each pixel's value may be computed separately.  Also, image reconstruction may begin as soon as the first projection function is obtained, rather than waiting for all projections to be collected.  Finally, the filtered backprojection algorithm does not depend on uniform angular sampling in the projection data, and multiple object rotations or platform passes can be used.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting the library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the range to the center of the image (m), the **x** location of the target, they **y** location of the target, the target RCS (m^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_center = 1e3\n",
    "\n",
    "xt = [-3.0, 4.0]\n",
    "\n",
    "yt = [1.0, -2.0]\n",
    "\n",
    "rt = [1.0, 10.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set image span in the **x** and **y** directions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_span = 12.0\n",
    "\n",
    "y_span = 12.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the number of bins in the **x** and **y** directions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 200\n",
    "\n",
    "ny = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the start freuqency (Hz) and the bandwidth (Hz) of the transmitted signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_frequency = 1e9\n",
    "\n",
    "bandwidth = 100e6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the azimuth space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.constants import c\n",
    "\n",
    "from numpy import sqrt, linspace, radians\n",
    "\n",
    "\n",
    "az_start = -3.0\n",
    "\n",
    "az_end = 3.0\n",
    "\n",
    "\n",
    "r = sqrt(x_span ** 2 + y_span ** 2)\n",
    "\n",
    "da = c / (2.0 * r * start_frequency)\n",
    "\n",
    "na = int(radians(az_end - az_start) / da)\n",
    "\n",
    "az = linspace(az_start, az_end, na)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the frequency space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.fftpack import next_fast_len\n",
    "\n",
    "\n",
    "df = c / (2.0 * r)\n",
    "\n",
    "nf = int(bandwidth / df)\n",
    "\n",
    "frequency = linspace(start_frequency, start_frequency + bandwidth, nf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the FFT length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.fftpack import next_fast_len\n",
    "\n",
    "fft_length = 8 * next_fast_len(nf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the aperture positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import sin, cos, radians, zeros_like\n",
    "\n",
    "sensor_x = range_center * cos(radians(az))\n",
    "\n",
    "sensor_y = range_center * sin(radians(az))\n",
    "\n",
    "sensor_z = zeros_like(sensor_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the image space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import meshgrid\n",
    "\n",
    "\n",
    "xi = linspace(-0.5 * x_span, 0.5 * x_span, nx)\n",
    "\n",
    "yi = linspace(-0.5 * y_span, 0.5 * y_span, ny)\n",
    "\n",
    "x_image, y_image = meshgrid(xi, yi)\n",
    "\n",
    "z_image = zeros_like(x_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the signal (k-space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import zeros, dot, exp\n",
    "\n",
    "from scipy.constants import pi\n",
    "\n",
    "signal = zeros([nf, na], dtype=complex)\n",
    "\n",
    "\n",
    "index = 0\n",
    "\n",
    "for a in az:\n",
    "\n",
    "    r_los = [cos(radians(a)), sin(radians(a))]\n",
    "\n",
    "\n",
    "    for x, y, r in zip(xt, yt, rt):\n",
    "\n",
    "        r_target = -dot(r_los, [x, y])\n",
    "\n",
    "        signal[:, index] += r * exp(-1j * 4.0 * pi * frequency / c * r_target)\n",
    "\n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the window coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.signal.windows import hann, hamming\n",
    "\n",
    "from numpy import outer, ones\n",
    "\n",
    "\n",
    "window_type = 'Hanning'\n",
    "\n",
    "\n",
    "\n",
    "if window_type == 'Hanning':\n",
    "\n",
    "    h1 = hann(nf, True)\n",
    "\n",
    "    h2 = hann(na, True)\n",
    "\n",
    "    coefficients = sqrt(outer(h1, h2))\n",
    "\n",
    "elif window_type == 'Hamming':\n",
    "\n",
    "    h1 = hamming(nf, True)\n",
    "\n",
    "    h2 = hamming(na, True)\n",
    "\n",
    "    coefficients = sqrt(outer(h1, h2))\n",
    "\n",
    "elif window_type == 'Rectangular':\n",
    "\n",
    "    coefficients = ones([nf, na])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the windowing coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal *= coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reconstruct the image using the `backprojection` routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.sar import backprojection\n",
    "\n",
    "bp_image = backprojection.reconstruct(signal, sensor_x, sensor_y, sensor_z, range_center, x_image, y_image, z_image, frequency, fft_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the dynamic range for the image (dB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dynamic_range = 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the backprojection image using the `matplotlib` routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from numpy import log10, amax\n",
    "\n",
    "\n",
    "# Normalize the image\n",
    "\n",
    "bpi = abs(bp_image) / amax(abs(bp_image))\n",
    "\n",
    "\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "# Set up the axes\n",
    "\n",
    "fig, axes1 = plt.subplots()\n",
    "\n",
    "\n",
    "# create the color plot\n",
    "\n",
    "im = axes1.pcolor(xi, yi, 20.0 * log10(bpi), cmap='jet', vmin=-dynamic_range, vmax=0, shading = 'auto')\n",
    "\n",
    "cbar = fig.colorbar(im, ax=axes1, orientation='vertical')\n",
    "\n",
    "cbar.set_label(\"(dB)\", size=10)\n",
    "\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "axes1.set_title('Back Projection', size=14)\n",
    "\n",
    "axes1.set_xlabel('Range (m)', size=12)\n",
    "\n",
    "axes1.set_ylabel('Cross Range (m)', size=12)\n",
    "\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "axes1.tick_params(labelsize=12)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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 "nbformat": 4,
 "nbformat_minor": 4
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